{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[173, 184, 129, 197, 95, 135, 96, 298, 113, 164, 68, 98, 95, 47, 143, 97, 178, 101, 253, 169, 288, 66]\n",
      "[0, 173, 357, 486, 683, 778, 913, 1009, 1307, 1420, 1584, 1652, 1750, 1845, 1892, 2035, 2132, 2310, 2411, 2664, 2833, 3121]\n",
      "[0.3035556674003601, 0.2987757921218872, 0.30279234051704407, 0.30142056941986084, 0.2968868911266327, 0.29766929149627686, 0.2973047196865082, 0.29762953519821167, 0.3001158833503723, 0.29855090379714966, 0.30221620202064514, 0.2971709966659546, 0.30065008997917175, 0.3005460202693939, 0.2995036244392395, 0.2984800338745117, 0.2996758818626404, 0.2994350492954254, 0.2987256944179535, 0.29940328001976013, 0.30631038546562195, 0.36108171939849854]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "f = open('./evaluate_code/info.pkl','rb')\n",
    "e = pickle.load(f)\n",
    "# e = [[left,right,score,0/1],...]\n",
    "# print(e)\n",
    "# print(type(e))\n",
    "\n",
    "x = []\n",
    "score = []\n",
    "color = []\n",
    "width = []\n",
    "for i in e:\n",
    "    score.append(i[2])\n",
    "    if i[3]==1:\n",
    "        color.append('mediumpurple')\n",
    "    else:\n",
    "        color.append('cornflowerblue')\n",
    "    x.append(i[0])\n",
    "    width.append(i[1]-i[0])\n",
    "print(width)\n",
    "print(x)\n",
    "assert len(x)==len(score)==len(width)==len(color)\n",
    "\n",
    "print(score)\n",
    "plt.bar(x, score, width=width, color=color, align='edge')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# h5 文件的内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['fea_1', 'fea_10', 'fea_11', 'fea_12', 'fea_13', 'fea_14', 'fea_15', 'fea_16', 'fea_17', 'fea_18', 'fea_19', 'fea_2', 'fea_20', 'fea_21', 'fea_22', 'fea_23', 'fea_24', 'fea_25', 'fea_26', 'fea_27', 'fea_28', 'fea_29', 'fea_3', 'fea_30', 'fea_31', 'fea_32', 'fea_33', 'fea_34', 'fea_35', 'fea_36', 'fea_37', 'fea_38', 'fea_39', 'fea_4', 'fea_40', 'fea_41', 'fea_42', 'fea_43', 'fea_44', 'fea_45', 'fea_46', 'fea_47', 'fea_48', 'fea_49', 'fea_5', 'fea_50', 'fea_6', 'fea_7', 'fea_8', 'fea_9', 'gt_1_1', 'gt_1_10', 'gt_1_11', 'gt_1_12', 'gt_1_13', 'gt_1_14', 'gt_1_15', 'gt_1_16', 'gt_1_17', 'gt_1_18', 'gt_1_19', 'gt_1_2', 'gt_1_20', 'gt_1_21', 'gt_1_22', 'gt_1_23', 'gt_1_24', 'gt_1_25', 'gt_1_26', 'gt_1_27', 'gt_1_28', 'gt_1_29', 'gt_1_3', 'gt_1_30', 'gt_1_31', 'gt_1_32', 'gt_1_33', 'gt_1_34', 'gt_1_35', 'gt_1_36', 'gt_1_37', 'gt_1_38', 'gt_1_39', 'gt_1_4', 'gt_1_40', 'gt_1_41', 'gt_1_42', 'gt_1_43', 'gt_1_44', 'gt_1_45', 'gt_1_46', 'gt_1_47', 'gt_1_48', 'gt_1_49', 'gt_1_5', 'gt_1_50', 'gt_1_6', 'gt_1_7', 'gt_1_8', 'gt_1_9', 'gt_2_1', 'gt_2_10', 'gt_2_11', 'gt_2_12', 'gt_2_13', 'gt_2_14', 'gt_2_15', 'gt_2_16', 'gt_2_17', 'gt_2_18', 'gt_2_19', 'gt_2_2', 'gt_2_20', 'gt_2_21', 'gt_2_22', 'gt_2_23', 'gt_2_24', 'gt_2_25', 'gt_2_26', 'gt_2_27', 'gt_2_28', 'gt_2_29', 'gt_2_3', 'gt_2_30', 'gt_2_31', 'gt_2_32', 'gt_2_33', 'gt_2_34', 'gt_2_35', 'gt_2_36', 'gt_2_37', 'gt_2_38', 'gt_2_39', 'gt_2_4', 'gt_2_40', 'gt_2_41', 'gt_2_42', 'gt_2_43', 'gt_2_44', 'gt_2_45', 'gt_2_46', 'gt_2_47', 'gt_2_48', 'gt_2_49', 'gt_2_5', 'gt_2_50', 'gt_2_6', 'gt_2_7', 'gt_2_8', 'gt_2_9', 'idx', 'ord']\n",
      "0.06789250353606789\n",
      "sels.shape (48,)\n",
      "ord: [ 1.  6. 11. 16. 21. 26. 31. 36. 41. 46.  2.  7. 12. 17. 22. 27. 32. 37.\n",
      " 42. 47.  3.  8. 13. 18. 23. 28. 33. 38. 43. 48.  4.  9. 14. 19. 24. 29.\n",
      " 34. 39. 44. 49.  5. 10. 15. 20. 25. 30. 35. 40. 45. 50.]\n",
      "idx: [ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.\n",
      " 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36.\n",
      " 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50.]\n",
      "youtube idx: [11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 23. 24. 25. 26. 27. 28. 29.\n",
      " 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47.\n",
      " 48. 49. 50.]\n",
      "youtube ord: [17. 15. 26. 21. 41. 31. 42. 49. 16. 11. 24. 37. 35. 40. 43. 44. 19. 12.\n",
      " 18. 32. 34. 36. 48. 50. 13. 25. 20. 33. 30. 28. 47. 45. 27. 14. 23. 39.\n",
      " 38. 29. 46.]\n",
      "(1024, 707)\n",
      "[0.475  0.475  0.475  0.475  0.175  0.175  0.175  0.175  0.0625 0.0625]\n",
      "[0.67857143 0.67857143 0.67857143 0.67857143 0.25       0.25\n",
      " 0.25       0.25       0.08928571 0.08928571]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zgp/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "filename = '../data/Data_TVSum_google_p5.h5'\n",
    "filename2 = '../data/Data_Youtube_google_p5.h5'\n",
    "f2 = h5py.File(filename2)\n",
    "f = h5py.File(filename)\n",
    "print(list(f.keys()))\n",
    "sel = np.array(f['gt_2_1'])\n",
    "sels =  sel[sel==1]\n",
    "print(sels.shape[0]/sel.shape[0])\n",
    "print('sels.shape', sels.shape)\n",
    "print('ord:', np.array(f['ord']).reshape((-1)))\n",
    "print('idx:', np.array(f['idx']).reshape((-1)))\n",
    "print('youtube idx:', np.array(f2['idx']).reshape((-1)))\n",
    "print('youtube ord:', np.array(f2['ord']).reshape((-1)))\n",
    "\n",
    "print(np.array(f['fea_1']).shape)\n",
    "sco = np.array(f['gt_1_1']).reshape((-1))\n",
    "print(sco[:10])\n",
    "ans = (sco-np.min(sco,axis=0))/(np.max(sco,axis=0) - np.min(sco,axis=0))\n",
    "print(ans[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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